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1. Consolidating Strategies for Countering Hate Speech Using Persuasive Dialogues

   Saha, Sougata; Srihari, Rohini K (December 2023, ACL Anthology)

   Hateful comments are prevalent on social media platforms. Although tools for automatically detecting, flagging, and blocking such false, offensive, and harmful content online have lately matured, such reactive and brute force methods alone provide short-term and superficial remedies while the perpetrators persist. With the public availability of large language models which can generate articulate synthetic and engaging content at scale, there are concerns about the rapid growth of dissemination of such malicious content on the web. There is now a need to focus on deeper, long-term solutions that involve engaging with the human perpetrator behind the source of the content to change their viewpoint or at least bring down the rhetoric using persuasive means. To do that, we propose defining and experimenting with controllable strategies for generating counterarguments to hateful comments in online conversations. We experiment with controlling response generation using features based on (i) argument structure and reasoning-based Walton argument schemes, (ii) counter-argument speech acts, and (iii) human characteristicsbased qualities such as Big-5 personality traits and human values. Using automatic and human evaluations, we determine the best combination of features that generate fluent, argumentative, and logically sound arguments for countering hate. We further share the developed computational models for automatically annotating text with such features, and a silver-standard annotated version of an existing hate speech dialog corpora. 

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2. Measuring and Characterizing Hate Speech on News Websites

   https://doi.org/10.1145/3394231.3397902  

   Zannettou, Savvas; Elsherief, Mai; Belding, Elizabeth; Nilizadeh, Shirin; Stringhini, Gianluca (June 2020, ACM Conference on Web Science)

   null (Ed.)

   The Web has become the main source for news acquisition. At the same time, news discussion has become more social: users can post comments on news articles or discuss news articles on other platforms like Reddit. These features empower and enable discussions among the users; however, they also act as the medium for the dissemination of toxic discourse and hate speech. The research community lacks a general understanding on what type of content attracts hateful discourse and the possible effects of social networks on the commenting activity on news articles. In this work, we perform a large-scale quantitative analysis of 125M comments posted on 412K news articles over the course of 19 months. We analyze the content of the collected articles and their comments using temporal analysis, user-based analysis, and linguistic analysis, to shed light on what elements attract hateful comments on news articles. We also investigate commenting activity when an article is posted on either 4chan’s Politically Incorrect board (/pol/) or six selected subreddits. We find statistically significant increases in hateful commenting activity around real-world divisive events like the “Unite the Right” rally in Charlottesville and political events like the second and third 2016 US presidential debates. Also, we find that articles that attract a substantial number of hateful comments have different linguistic characteristics when compared to articles that do not attract hateful comments. Furthermore, we observe that the post of a news articles on either /pol/ or the six subreddits is correlated with an increase of (hateful) commenting activity on the news articles. 

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3. This Paper Was Written with the Help of ChatGPT: Exploring the Consequences of AI-Driven Academic Writing on Scholarly Practices

   https://doi.org/10.5281/zenodo.12729880  

   Li, Hongming; Lee, Seiyon; Botelho, Anthony F (July 2024, International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   This paper was written with the help of ChatGPT. Recent advancements in the development and deployment of large generative language models to power generative AI tools, including OpenAIż˝fs ChatGPT, have led to their broad usage across virtually all fields of study. While the tools have been trained to generate human-like-dialogue in response to questions or prompts, they are similarly used to compose larger, more complex artifacts, including social media posts, essays, and even research articles. Although this abstract has been written entirely by a human without any input, consultation, or revision from a generative language model, it would likely be difficult to discern any difference as a reader. In light of this, there is growing debate and concern regarding using these models to aid the writing process, particularly concerning publication. Aside from some notable risks, including the unintentional generation of false information, citation of non-existing research articles, or plagiarism by generating text that is sampled from another source without proper citation, there are additional questions pertaining to the originality of ideas expressed in a work has been partially-written or revised by a generative language model. We present this paper as both a case study into the usage of generative models to aid in the writing of academic research articles but also as an example of how transparency and open science practices may help in addressing several issues that have been raised in other contexts and communities. While this paper neither attempts to promote nor contest the use of these language models in any writing task, it is the goal of this work to provide insight and potential guidance into the ethical and effective usage of these models within this domain. 

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4. THOS: A Benchmark Dataset for Targeted Hate and Offensive Speech

   Almohaimeed, S; Almohaimeed, S; Shafin, A A; Carbunar, B; Bölöni, L (July 2023, Proc. of Data-centric Machine Learning Research (DMLR) Workshop at ICML 2023)

   Detecting harmful content on social media, such as Twitter, is made difficult by the fact that the seemingly simple yes/no classification conceals a significant amount of complexity. Unfortunately, while several datasets have been collected for training classifiers in hate and offensive speech, there is a scarcity of datasets labeled with a finer granularity of target classes and specific targets. In this paper, we introduce THOS, a dataset of 8.3k tweets manually labeled with fine-grained annotations about the target of the message. We demonstrate that this dataset makes it feasible to train classifiers, based on Large Language Models, to perform classification at this level of granularity. 

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5. Bias Mitigation for Toxicity Detection via Sequential Decisions

   https://doi.org/10.1145/3477495.3531945  

   Cheng, L.; Mosallanezhad, A.; Silva, Y. N.; Hall, D. (January 2022, The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR))

   Increased social media use has contributed to the greater prevalence of abusive, rude, and offensive textual comments. Machine learning models have been developed to detect toxic comments online, yet these models tend to show biases against users with marginalized or minority identities (e.g., females and African Americans). Established research in debiasing toxicity classifiers often (1) takes a static or batch approach, assuming that all information is available and then making a one-time decision; and (2) uses a generic strategy to mitigate different biases (e.g., gender and racial biases) that assumes the biases are independent of one another. However, in real scenarios, the input typically arrives as a sequence of comments/words over time instead of all at once. Thus, decisions based on partial information must be made while additional input is arriving. Moreover, social bias is complex by nature. Each type of bias is defined within its unique context, which, consistent with intersectionality theory within the social sciences, might be correlated with the contexts of other forms of bias. In this work, we consider debiasing toxicity detection as a sequential decision-making process where different biases can be interdependent. In particular, we study debiasing toxicity detection with two aims: (1) to examine whether different biases tend to correlate with each other; and (2) to investigate how to jointly mitigate these correlated biases in an interactive manner to minimize the total amount of bias. At the core of our approach is a framework built upon theories of sequential Markov Decision Processes that seeks to maximize the prediction accuracy and minimize the bias measures tailored to individual biases. Evaluations on two benchmark datasets empirically validate the hypothesis that biases tend to be correlated and corroborate the effectiveness of the proposed sequential debiasing strategy. 

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